Developing an Intelligent Decision-Support Model for Risk Management: A Case Study of the Iranian Healthcare Industry

Abstract

The present study aimed to design and explain an “Intelligent Decision-Support Model for Risk Management,” using the Iranian healthcare industry as a case study. This study adopted a qualitative approach and employed grounded theory methodology based on the systematic approach of Strauss and Corbin. The study population consisted of experts in healthcare facility construction, senior managers of hospital construction projects, risk management specialists, and artificial intelligence experts. Purposive and theoretical sampling was employed and continued until theoretical saturation was achieved, resulting in the participation of 18 experts. Data were collected through semi-structured interviews and analyzed through the three stages of open, axial, and selective coding. Analysis of the qualitative data led to the extraction of a paradigmatic model centered on the core category of the “Intelligent Decision-Support Model for Risk Management,” comprising six dimensions and 30 axial codes. Specifically, the causal conditions included “the necessity and strategic importance of the construction industry,” “managerial and analytical capacity,” “fundamental weaknesses in risk management,” “technological transformation and intelligentization,” and “data-driven and future-oriented requirements,” which were identified as the fundamental drivers underlying the formation of the system. The central phenomenon of the study was manifested through the components of “decision support under conditions of uncertainty,” “intelligent risk identification and assessment,” “processing and interpretation of project information,” “risk prediction and prioritization,” and “integration of decision-support logic.” In response to this phenomenon, operational strategies were formulated, including “development and localization of technological solutions,” “testing and gradual system deployment,” “proactive risk design and response,” “project learning and knowledge accumulation,” and “participation and utilization of expertise.” The implementation of these strategies is facilitated by contextual factors such as “organizational structure and culture,” “technological maturity and readiness,” “technological capability and infrastructure,” “data ecosystem and information security,” and “the institutional and geographical environment of the project.” Furthermore, this process is influenced by intervening factors including “weaknesses in human and managerial capital,” “barriers to technology adoption and implementation,” “financial constraints and managerial stability,” “intra-project and supply-chain disruptions,” and “environmental and external pressures.” Ultimately, implementing the proposed model leads to tangible outcomes, including “enhanced decision-making quality and confidence,” “improved risk management performance,” “project operational efficiency,” “enhanced resource utilization and implementation quality,” and “long-term learning and sustainability” within the national healthcare industry.